Method and system for classifying automobile types based on neural network

Abstract

The invention relates to the field of automobile classification technologies and discloses a method and system for classifying automobile types based on a neural network. The method comprises the steps that a plurality of training samples are collected, and the training samples are classified based on the convolutional neural network, so a classifier containing label results is obtained; when the automobile types are classified, a video image to be detected is read in, a motion object in the image is detected, and sub-block processing is performed on the image according to the motion object; afterwards, classification processing is performed on each block of image through the classifier, so a detection result is obtained. Accordingly, a neural network system can be constructed easily and conveniently to serve as the classifier, the system is trained by using different automobile samples, the system is made to automatically study complex class conditional density of the samples, and therefore the problems caused by class conditional density functions of artificial hypothesis are avoided. Compared with an existing automobile type classification method, the method for classifying the automobile types based on the convolutional neural network has the advantages that the accuracy of classification is improved, and the classification speed is increased.

Description

technical field [0001] The invention relates to the technical field of automobile classification, in particular to a method and a system for realizing classification of vehicle types by using a neural network. Background technique [0002] Car type classification refers to dividing cars into different car types according to different classification standards. Due to the existence of various interferences, such as the complexity of the surrounding environment, the interference of various factors such as weather, the classification of vehicle models has become a relatively complicated research problem. At present, the main problems in the research of vehicle classification technology are: the detection accuracy is not high, the detection speed is too slow, and the calculation amount is too large. For example: when a large number of vehicles pass through a certain road section, the difficulty of detection increases. If the real-time monitoring system is slow, it will not be ab...

AI Technical Summary

Problems solved by technology

Due to various interferences, such as the complexity of the surrounding environment, weather and other factors, the classification of vehicle models has become a relatively complicated research problem.

At present, the main problems in the research of vehicle classification technology are: the detection accuracy is not high, the detection speed is too slow, and the calculation amount is too large.

[0022] However, the existing vehicle classification methods still have certain deficiencies in the classification accuracy or speed, and the existing technology needs to be improved and improved.

Images